Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x1a5b441deb8>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x1a5b44da668>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_input')
    z_input = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

import collections

Shape = collections.namedtuple('Shape', ('width', 'height', 'depth'))

# Shapes (just for information)
mnist_shape = Shape(28, 28, 1)
celeba_shape = Shape(28, 28, 3)

import functools
import operator

def flatten_size(shape):
    """Calculates flatten size by Shape"""
    return functools.reduce(operator.mul, shape)

def flatten(x, conv_shape):
    """Converts convolutional layer to dense layer"""
    return tf.reshape(x, (-1, flatten_size(conv_shape)))                    

def leaky_relu(x, alpha):
    return tf.maximum(alpha * x, x)

DISCRIMINATOR_VAR_SCOPE = 'discriminator'


def convout(input_tensor, alpha, training, normalization=True, dropout=None):
    if normalization:
        input_tensor = tf.layers.batch_normalization(input_tensor, training=training)
    relu = leaky_relu(input_tensor, alpha)
    if dropout:
        out = tf.layers.dropout(relu, dropout, training=training)
    else:
        out = relu
    return out

                      
def discriminator(images, reuse=False, alpha=0.2, rate=0.1, kernel_size=3):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    kernel_initializer = tf.contrib.layers.xavier_initializer_conv2d
    
    with tf.variable_scope(DISCRIMINATOR_VAR_SCOPE, reuse=reuse):
        # Input layer is 28x28x1 or 28x28x3
        
        x1 = tf.layers.conv2d(images, 128, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        out1 = convout(x1, alpha, training=True, normalization=False, dropout=rate)
        # 16x16x128 now
                
        x2 = tf.layers.conv2d(out1, 256, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        out2 = convout(x2, alpha, training=True, normalization=True, dropout=rate)
        # 8x8x256 now
        
        x3 = tf.layers.conv2d(out2, 512, kernel_size, strides=2, padding='same', kernel_initializer=kernel_initializer())
        out3 = convout(x3, alpha, training=True, normalization=True, dropout=rate)
        # 4x4x512 now

        out3_shape = Shape(*[int(dimension) for dimension in out3.shape[1:]])
        
        # Flatten it
        flat = flatten(out3, out3_shape)
        
        logits = tf.layers.dense(flat, 1, kernel_initializer=kernel_initializer())
        
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def start_convolutional(x, shape, kernel_initializer):
    """Starts convolutional stack
    
    shape is a Shape object
    """
    y = tf.layers.dense(x, flatten_size(shape), kernel_initializer=kernel_initializer)
    # Reshape y to start the convolutional stack
    y = tf.reshape(y, (-1, shape.width, shape.height, shape.depth))
    return y

GENERATOR_VAR_SCOPE = 'generator'

def generator(z, out_channel_dim, is_train=True, alpha=0.2, rate=0.1, kernel_size=5):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    
    kernel_initializer = tf.contrib.layers.xavier_initializer_conv2d
    
    with tf.variable_scope(GENERATOR_VAR_SCOPE, reuse=not is_train):

        x0 = start_convolutional(z, Shape(7, 7, 512), kernel_initializer())
        out0 = convout(x0, alpha, training=is_train, normalization=True, dropout=rate)
        # 7x7x512 now
        
        x1 = tf.layers.conv2d_transpose(out0, 256, kernel_size, strides=1, padding='same',
                                        kernel_initializer=kernel_initializer())
        out1 = convout(x1, alpha, training=is_train, normalization=True, dropout=rate)
        # 14x14x256 now
        
        x2 = tf.layers.conv2d_transpose(out1, 128, kernel_size, strides=2, padding='same',
                                        kernel_initializer=kernel_initializer())
        out2 = convout(x2, alpha, training=is_train, normalization=True, dropout=rate)
        # 28x28x128 now
        
        logits = tf.layers.conv2d_transpose(out2, out_channel_dim, kernel_size, strides=2, padding='same',
                                            kernel_initializer=kernel_initializer())
        # 28x28x<out>
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def one_sided_smoothing(out, k):
    return tf.ones_like(out) * (1 - k)

def uniform_smoothing(out, from_, to):
    return tf.random_uniform(tf.shape(out), from_, to, tf.float32)

def model_loss(input_real, input_z, out_channel_dim, alpha=0.2,
               label_smoothness=0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_out = generator(input_z, out_channel_dim, is_train=True, alpha=alpha)
    d_real_out, d_real_logits = discriminator(input_real, reuse=False, alpha=alpha)
    d_fake_out, d_fake_logits = discriminator(g_out, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_real_logits, 
            labels=uniform_smoothing(d_real_out, 0.7, 1.2)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_fake_logits, 
            labels=uniform_smoothing(d_fake_out, 0.0, 0.3)))
    
    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_fake_logits, 
            labels=tf.ones_like(d_fake_out)))
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith(DISCRIMINATOR_VAR_SCOPE)]
    g_vars = [var for var in t_vars if var.name.startswith(GENERATOR_VAR_SCOPE)]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, 
          alpha=0.2,
          print_every=10, show_every=50, show_images=25):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    _, image_width, image_height, image_channels = data_shape      
    
    # Build model
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)        
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=alpha)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    # let's go
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                steps += 1
                
                # images normalization [-1, 1]
                batch_images = batch_images * 2
                
                # white noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                 # Run optimizers
                _ = sess.run(d_opt,
                             feed_dict={input_z: batch_z,
                                        input_real: batch_images,
                                        lr: learning_rate})
                _ = sess.run(g_opt, 
                             feed_dict={input_z: batch_z, 
                                        input_real: batch_images,
                                        lr: learning_rate})
                                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    #Print the current image
                    show_generator_output(sess, show_images, input_z, image_channels, data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.0002
alpha = 0.2
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, alpha=alpha)

Epoch 1/2... Discriminator Loss: 1.3081... Generator Loss: 0.6911
Epoch 1/2... Discriminator Loss: 2.5795... Generator Loss: 0.1279
Epoch 1/2... Discriminator Loss: 1.1497... Generator Loss: 0.5747
Epoch 1/2... Discriminator Loss: 2.0989... Generator Loss: 0.1709
Epoch 1/2... Discriminator Loss: 1.0945... Generator Loss: 1.9049

Epoch 1/2... Discriminator Loss: 1.2526... Generator Loss: 0.5328
Epoch 1/2... Discriminator Loss: 1.3069... Generator Loss: 0.6882
Epoch 1/2... Discriminator Loss: 1.2032... Generator Loss: 1.0474
Epoch 1/2... Discriminator Loss: 1.0748... Generator Loss: 0.9425
Epoch 1/2... Discriminator Loss: 1.0631... Generator Loss: 0.8896

Epoch 1/2... Discriminator Loss: 1.0036... Generator Loss: 0.8629
Epoch 1/2... Discriminator Loss: 1.0036... Generator Loss: 0.9880
Epoch 1/2... Discriminator Loss: 1.3658... Generator Loss: 1.9693
Epoch 1/2... Discriminator Loss: 1.4980... Generator Loss: 0.5140
Epoch 1/2... Discriminator Loss: 1.3983... Generator Loss: 0.5151

Epoch 1/2... Discriminator Loss: 1.3283... Generator Loss: 0.7177
Epoch 1/2... Discriminator Loss: 1.3398... Generator Loss: 0.4529
Epoch 1/2... Discriminator Loss: 1.2627... Generator Loss: 0.7056
Epoch 1/2... Discriminator Loss: 1.4594... Generator Loss: 0.5156
Epoch 1/2... Discriminator Loss: 1.2311... Generator Loss: 0.9564

Epoch 1/2... Discriminator Loss: 1.2756... Generator Loss: 0.9564
Epoch 1/2... Discriminator Loss: 1.1317... Generator Loss: 0.9484
Epoch 1/2... Discriminator Loss: 1.2730... Generator Loss: 1.3497
Epoch 1/2... Discriminator Loss: 1.3344... Generator Loss: 0.6232
Epoch 1/2... Discriminator Loss: 1.1524... Generator Loss: 0.6749

Epoch 1/2... Discriminator Loss: 1.2924... Generator Loss: 1.5185
Epoch 1/2... Discriminator Loss: 1.1524... Generator Loss: 0.7882
Epoch 1/2... Discriminator Loss: 1.3398... Generator Loss: 0.5958
Epoch 1/2... Discriminator Loss: 1.0905... Generator Loss: 0.6365
Epoch 1/2... Discriminator Loss: 1.1239... Generator Loss: 1.1979

Epoch 1/2... Discriminator Loss: 1.0791... Generator Loss: 1.0262
Epoch 1/2... Discriminator Loss: 1.1971... Generator Loss: 0.7471
Epoch 1/2... Discriminator Loss: 1.0173... Generator Loss: 0.8705
Epoch 1/2... Discriminator Loss: 1.1019... Generator Loss: 1.0515
Epoch 1/2... Discriminator Loss: 1.1708... Generator Loss: 0.6962

Epoch 1/2... Discriminator Loss: 1.2216... Generator Loss: 0.6151
Epoch 1/2... Discriminator Loss: 1.1131... Generator Loss: 0.6627
Epoch 1/2... Discriminator Loss: 1.0663... Generator Loss: 0.7602
Epoch 1/2... Discriminator Loss: 1.1104... Generator Loss: 0.7230
Epoch 1/2... Discriminator Loss: 1.1574... Generator Loss: 0.8214

Epoch 1/2... Discriminator Loss: 1.0537... Generator Loss: 0.9490
Epoch 1/2... Discriminator Loss: 1.1076... Generator Loss: 1.2220
Epoch 1/2... Discriminator Loss: 1.1130... Generator Loss: 0.8588
Epoch 1/2... Discriminator Loss: 1.1194... Generator Loss: 0.9811
Epoch 1/2... Discriminator Loss: 0.9725... Generator Loss: 0.9919

Epoch 1/2... Discriminator Loss: 1.1245... Generator Loss: 1.4139
Epoch 1/2... Discriminator Loss: 1.0596... Generator Loss: 0.8623
Epoch 1/2... Discriminator Loss: 0.9929... Generator Loss: 1.0218
Epoch 1/2... Discriminator Loss: 1.1030... Generator Loss: 1.0912
Epoch 1/2... Discriminator Loss: 1.0247... Generator Loss: 0.8004

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 64
z_dim = 100
learning_rate = 0.0002
alpha = 0.2
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha=alpha)

Epoch 1/1... Discriminator Loss: 1.0744... Generator Loss: 1.2621
Epoch 1/1... Discriminator Loss: 0.7676... Generator Loss: 1.4217
Epoch 1/1... Discriminator Loss: 1.4787... Generator Loss: 4.1805
Epoch 1/1... Discriminator Loss: 2.2720... Generator Loss: 0.1531
Epoch 1/1... Discriminator Loss: 1.3444... Generator Loss: 0.6630

Epoch 1/1... Discriminator Loss: 0.8700... Generator Loss: 1.4246
Epoch 1/1... Discriminator Loss: 0.8130... Generator Loss: 1.7064
Epoch 1/1... Discriminator Loss: 1.1111... Generator Loss: 3.5978
Epoch 1/1... Discriminator Loss: 1.0246... Generator Loss: 0.9188
Epoch 1/1... Discriminator Loss: 1.0518... Generator Loss: 0.7809

Epoch 1/1... Discriminator Loss: 1.0546... Generator Loss: 0.8261
Epoch 1/1... Discriminator Loss: 1.1048... Generator Loss: 0.7776
Epoch 1/1... Discriminator Loss: 0.9568... Generator Loss: 1.2712
Epoch 1/1... Discriminator Loss: 0.9982... Generator Loss: 0.6972
Epoch 1/1... Discriminator Loss: 1.0999... Generator Loss: 1.4650

Epoch 1/1... Discriminator Loss: 1.4880... Generator Loss: 0.4239
Epoch 1/1... Discriminator Loss: 1.0600... Generator Loss: 0.8684
Epoch 1/1... Discriminator Loss: 1.6013... Generator Loss: 0.2777
Epoch 1/1... Discriminator Loss: 1.1913... Generator Loss: 0.7053
Epoch 1/1... Discriminator Loss: 1.0059... Generator Loss: 1.1483

Epoch 1/1... Discriminator Loss: 0.9553... Generator Loss: 1.2003
Epoch 1/1... Discriminator Loss: 1.0694... Generator Loss: 0.9285
Epoch 1/1... Discriminator Loss: 0.9982... Generator Loss: 1.1893
Epoch 1/1... Discriminator Loss: 1.2526... Generator Loss: 0.6446
Epoch 1/1... Discriminator Loss: 0.9747... Generator Loss: 1.8727

Epoch 1/1... Discriminator Loss: 1.1012... Generator Loss: 1.4135
Epoch 1/1... Discriminator Loss: 0.9103... Generator Loss: 1.0902
Epoch 1/1... Discriminator Loss: 1.7225... Generator Loss: 0.3694
Epoch 1/1... Discriminator Loss: 1.1034... Generator Loss: 0.8011
Epoch 1/1... Discriminator Loss: 0.9762... Generator Loss: 1.2393

Epoch 1/1... Discriminator Loss: 0.9951... Generator Loss: 2.2145
Epoch 1/1... Discriminator Loss: 1.0593... Generator Loss: 1.7312
Epoch 1/1... Discriminator Loss: 0.9189... Generator Loss: 1.0367
Epoch 1/1... Discriminator Loss: 0.8937... Generator Loss: 1.0464
Epoch 1/1... Discriminator Loss: 0.8737... Generator Loss: 1.3944

Epoch 1/1... Discriminator Loss: 0.9516... Generator Loss: 1.1199
Epoch 1/1... Discriminator Loss: 1.0809... Generator Loss: 1.5612
Epoch 1/1... Discriminator Loss: 1.1001... Generator Loss: 0.7071
Epoch 1/1... Discriminator Loss: 1.1362... Generator Loss: 2.1655
Epoch 1/1... Discriminator Loss: 1.1825... Generator Loss: 0.6215

Epoch 1/1... Discriminator Loss: 0.8540... Generator Loss: 1.0786
Epoch 1/1... Discriminator Loss: 0.8664... Generator Loss: 1.4316
Epoch 1/1... Discriminator Loss: 0.9095... Generator Loss: 2.0778
Epoch 1/1... Discriminator Loss: 0.8505... Generator Loss: 1.1733
Epoch 1/1... Discriminator Loss: 1.1240... Generator Loss: 2.3303

Epoch 1/1... Discriminator Loss: 1.0757... Generator Loss: 0.9983
Epoch 1/1... Discriminator Loss: 1.0740... Generator Loss: 0.6254
Epoch 1/1... Discriminator Loss: 1.0411... Generator Loss: 0.7549
Epoch 1/1... Discriminator Loss: 0.8241... Generator Loss: 1.4696
Epoch 1/1... Discriminator Loss: 1.1740... Generator Loss: 1.6477

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.